With process computers routinely collecting measurements on large numbers of process variables, multivariate statistical methods for the analysis, monitoring and diagnosis of process operating performance have received increasing attention. Recent approaches to multivariate statistical process control, which utilize not only the product quality data (as traditional approaches have done) but also the available process data, are based on multivariate projection methods (Principal Component Analysis, PCA, and Partial Least Squares, PLS). These methods have been rapidly accepted and utilized by industry. This paper gives a brief overview of these methods and illustrates their use for process monitoring and fault diagnosis with applications to a wide range of industrial batch and continuous processes. Emphasis is placed on the practical issues that arise when dealing with process data. Several of these issues are discussed and solutions are suggested for a successful outcome of the application of these methods in an industrial setting.